2 papers
cs.CV2026
Mitigating Bias in Concept Bottleneck Models for Fair and Interpretable Image Classification
Schrasing Tong, Antoine Salaun, Vincent Yuan +2
Ensuring fairness in image classification prevents models from perpetuating and amplifying bias. Concept bottleneck models (CBMs) map images to high-level, human-interpretable conc…
cs.CV2025
Pairwise Matching of Intermediate Representations for Fine-grained Explainability
Lauren Shrack, Timm Haucke, Antoine Salaün +2
The differences between images belonging to fine-grained categories are often subtle and highly localized, and existing explainability techniques for deep learning models are often…